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Record W4409282631 · doi:10.1115/1.4068390

Master (Spark Plasma) Sintering Curve of UO2 and UO2-10 Vol. % Mo Fuel Material

2025· article· en· W4409282631 on OpenAlexaff
Anil Prasad, Murali Krishna Tummalapalli, Linu Malakkal, Lukas Bichler, Jerzy A. Szpunar, Charles Liu

Bibliographic record

VenueJournal of Nuclear Engineering and Radiation Science · 2025
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsUniversity of SaskatchewanCanadian Nuclear LaboratoriesOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsSpark plasma sinteringMaterials scienceSinteringPlasmaMetallurgySPARK (programming language)Nuclear engineeringNuclear physicsComputer sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

Abstract UO2-molybdenum (Mo) composites are being considered for accident tolerant fuel (ATF) applications in commercial nuclear reactors. In this study, UO2-10 vol. % Mo was fabricated using spark plasma sintering (SPS), where the particle size of Mo was varied between 30 nm, 60 nm, 100 nm, and 150 μm, to study its effect on the densification of the UO2-10 vol. % Mo composite. A decrease in Mo particle size was identified to cause a shift in densification peaks in UO2-Mo toward higher temperature, due to the Mo particles coating the bigger UO2 particles. The in situ ram displacement data was used to construct a master sintering curve (MSC), which was validated using additional SPS trials. The accuracy of the MSC was measured by the standard deviation between prediction and experimentally observed data, which varied from 0.7% to 4.8%.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.250
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.217
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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